svnm 1.4.2

A package to make the usage of DeepLearning models easier

svnm - A Simplified Deep Learning Models Package

svnm is a Python package designed to simplify the process of using deep learning models in your code. With just a simple import, you can easily load pre-trained models and make predictions with minimal setup. This package is aimed at making deep learning models more accessible for developers, enabling rapid experimentation and deployment.

Routing

Image Classification

• Gender Detection
• Face Detection
• Face Recognition
• Handwritten Digit Recognition

Features

• Easy-to-use interface: Import the package and start making predictions with minimal code.
• Pre-trained models: Includes popular deep learning models ready for use without additional training.
• Flexible prediction methods: Support for various types of input data to make predictions.
• Lightweight and efficient: Focuses on simplifying deep learning workflows without overhead.

Installation

To install svnm, simply use pip:

pip install svnm

Model Usage

Gender Detection

from svnm.models import GenderDetection

model = GenderDetection()

# Single Prediction
prediction, confidence = model.predict("face_image.jpg")
print(f"Predicted Gender: {prediction} with confidence {confidence:.2f}")

# Batch Prediction
image_paths = ["face1.jpg", "face2.jpg", "face3.jpg"]
predictions = model.predict_batch(image_paths)
for img, (label, conf) in zip(image_paths, predictions):
    print(f"Image: {img} -> Predicted Gender: {label} with confidence {conf:.2f}")

# Visualization
model.visualize_prediction("face_image.jpg")

How Gender Detection Works

The Gender Detection model in svnm classifies whether a detected face belongs to a male or female. It works as follows:

1. Loading the Model: The pre-trained deep learning model is loaded automatically when the GenderDetection class is initialized.
2. Processing the Image: The input image is preprocessed and resized before being fed into the model.
3. Making Predictions: The model analyzes the facial features and predicts the gender along with a confidence score.
4. Batch Processing:
   • Supports batch predictions for multiple images at once.
   • Each image is processed individually, and the results are returned as a list.
5. Visualization:
   • If visualize_prediction() is called, the image is displayed with the predicted gender and confidence score.

Face Detection

from svnm.models import FaceDetection

model = FaceDetection(save=True, save_dir="predictions")

# Single Image Prediction
predictions = model.predict("face_image.jpg")
print("Face Detection Results:", predictions)

# Batch Prediction
image_paths = ["face1.jpg", "face2.jpg", "face3.jpg"]
batch_predictions = model.predict_batch(image_paths)
for img, preds in zip(image_paths, batch_predictions):
    print(f"Image: {img} -> Predictions: {preds}")

How Face Detection Works

The Face Detection model in svnm is designed to detect human faces in images. It works as follows:

1. Loading the Model: The pre-trained YOLO-based face detection model is loaded automatically when the FaceDetection class is initialized.
2. Processing the Image: The input image is read, preprocessed, and fed into the model.
3. Detecting Faces: The model detects faces in the image and returns bounding box coordinates, confidence scores, and class labels.
4. Batch Processing:
   • Supports batch predictions for multiple images at once.
   • Each image is processed individually, and the results are returned as a list.
5. Saving and Visualizing Results:
   • If save=True, the detected faces are highlighted in the image and saved to the specified directory.
   • The model outputs a list of detected faces with their positions and confidence levels.

FaceRecognition Class

FaceRecognition(models=None)

• Description: Initialize the class with a list of DeepFace models. Default models are: ["Facenet", "OpenFace"].
• Parameters:
  • models (optional): List of model names to use.

detect_face(image_path, show=True)

• Description: Detects faces in an image using OpenCV Haar Cascade.
• Parameters:
  • image_path: Path to the image.
  • show: If True, displays the detected face(s).
• Returns: Tuple (faces, image_with_rectangles)

get_combined_embedding(image_path)

• Description: Extracts embeddings from selected models and calculates:
  • Average embedding
  • Concatenated embedding
  • Standard deviation embedding
• Returns:

  {
      "avg": np.array,
      "concat": np.array,
      "std": np.array
  }
  

compare_embeddings(embedding1, embedding2)

• Description: Compares two embeddings using cosine similarity.
• Parameters:
  • embedding1: Output of get_combined_embedding
  • embedding2: Output of get_combined_embedding
• Returns: Dictionary of similarity scores for each method:
  • "avg", "concat", and "std"

train(df, save=False, json_path="trained_data.json")

• Description: Trains the recognizer with a DataFrame of image paths and labels.
• Parameters:
  • df: Pandas DataFrame with path and label columns.
  • save: If True, saves the result to a JSON file.
  • json_path: Path to output JSON file.

find_best_match(image, source_type=None)

• Description: Finds the best matching label for a new image using cosine similarity on concat embeddings.
• Parameters:
  • image: Image path or URL
  • source_type: 'url' or 'path'. Defaults to 'path'
• Returns: Tuple: (predicted_label, similarity_score)

Handwritten Digit Recognition

from svnm.models import HandwrittenDigitRecognition

model = HandwrittenDigitRecognition()

# Single Image Prediction
prediction = model.predict("digit_image.jpg")
print("Predicted Digit:", prediction)

How Handwritten Digit Recognition Works

1. Loading the Model: The pre-trained digit recognition model is initialized.
2. Processing the Image: The input image is resized and normalized before being fed into the model.
3. Making Predictions:
   • The model predicts the digit and outputs the confidence score.
   • Supports batch predictions for multiple images at once.

Example Output

{"digit": 7, "confidence": 0.98}